PyTorch
is a machine learning library for Python that allows users to build
deep neural networks with great flexibility. Its easy to use API and
seamless use of GPUs make it a sought after tool for deep learning. This
course will introduce the PyTorch workflow and demonstrate how to use
it. Students will be equipped with the knowledge to build deep learning
models using real-world datasets.

Deep Learning with TensorFlow

Season Yang (McKinsey & Company)

The
TensorFlow library provides for the use of computational graphs, with
automatic parallelization across resources. This architecture is ideal
for implementing neural networks. This training will introduce
TensorFlow's capabilities in Python. It will move from building machine
learning algorithms piece by piece to using the Keras API provided by
TensorFlow with several hands-on applications.

Undesired
bias in machine learning has become a worrying topic due to the
numerous high profile incidents. In this talk we demystify machine
learning bias through a hands-on example. We'll be tasked to automate
the loan approval process for a company, and introduce key tools and
techniques from latest research that allow us to assess and mitigate
undesired bias in our machine learning models.

Design thinking for AI

Chris Butler (Philosophie)

Purpose,
a well-defined problem, and trust from people are important factors to
any system, especially those that employ AI. Chris Butler leads you
through exercises that borrow from the principles of design thinking to
help you create more impactful solutions and better team alignment.

基于深度学习的时间序列预测 (Deep learning for time series forecasting）

Yijing Chen (Microsoft)

Dmitry Pechyoni (Microsoft)

Angus Taylor (Microsoft)

Vanja Paunic (Microsoft)

Henry Zeng (Microsoft)

Almost
every business today uses forecasting to make better decisions and
allocate resources more effectively. Deep learning has achieved a lot of
success in computer vision, text and speech processing, but has only
recently been applied to time series forecasting. In this tutorial we
show how and when to apply deep neural networks to time series
forecasting. The tutorial will be in CHN and EN.

Intelligent
experiences powered by AI can seem like magic to users. Developing
them, however, is pretty cumbersome involving a series of sequential and
interconnected decisions along the way that are pretty time consuming.
What if there was an automated service that identifies the best machine
learning pipelines for a given problem/data? Automated machine learning
does exactly that!

Analytics Zoo: Distributed Tensorflow and Keras on Apache Spark

Zhichao Li (Intel)

In
this tutorial, we will show how to build and productionize deep
learning applications for Big Data using "Analytics
Zoo":https://github.com/intel-analytics/analytics-zoo (a unified
analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras
and BigDL programs into an integrated pipeline) using real-world use
cases (such as JD.com, MLSListings, World Bank, Baosight, Midea/KUKA,
etc.)

Building reinforcement learning models and AI applications with Ray

Richard Liaw (UC Berkeley RISELab)

Ray
is a general purpose framework for programming your cluster. We will
lead a deep dive into Ray, walking you through its API and system
architecture and sharing application examples, including several
state-of-the-art AI algorithms.

Forecasting customer activities with dilated convolution neural networks: Use case and best practices

Tao Lu (Microsoft)

Chenhui Hu (Microsoft)

Forecasting
customer activities is one of the most important and common business
problems. In Microsoft Azure Identity team, we forecast customer
behavior based on billions of user activities. We will share how we
improve 25% of forecasting accuracy with dilated convolutional neural
networks and reduce 80% of the time in development with the best
practices of time series forecasting.

Efficient deep learning for the edge

Bichen Wu (UC Berkeley)

The
success of deep neural networks is attributed to three factors:
stronger computing capacity, more complex neural networks, and more
data. These factors, however, are usually not available with the edge
applications as autonomous driving, AR/VR, IoT, and so on. In this talk
we discuss how we apply AutoML, SW/HW codesign, domain adaptation to
solve these problems.

TensorFlow lite for microcontrollers

Pete Warden (Google)

Pete Warden explores how you can use Google's open source framework to run machine learning models on embedded processors like microcontrollers and DSPs. Discover what you need to get started using the code itself, including hardware, coding tools, and getting the library built.

Using deep learning and time-series forecasting to reduce transit delays

Mark Ryan (IBM),

Alina Li Zhang (Nobul)

Toronto
is unique among North American cities for having a legacy streetcar
network as an integral part of its transit system. This means streetcar
delays are a major contributor to gridlock in the city. Using deep
learning and time-series forecasting, we'll show how streetcar delays
can be predicted... and prevented.

AI技术在外卖个性化场景中的落地与思考

ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX)

Henry Zeng (Microsoft)

Emma Ning (Microsoft)

An
open and interoperable ecosystem enables you to choose the framework
that's right for you, train at scale, and deploy to cloud and edge. ONNX
provides a common format supported by many popular frameworks and
hardware accelerators. This session provides an introduction to ONNX and
its core concepts. The session will be delivered in English and Chinese
jointly.

Exciting new features in TensorFlow 2.0

Tiezhen Wang (Google)

TensorFlow
2.0 is a major milestone with a focus on ease of use. This talk will
give a in depth introduction to the new exciting features and best
practices. Topics such as distributed strategies and edge deployment
(TensorFlow Lite and TensorFlow.js) will also be covered.

打造A.I.闭环 引领产业变革

The unreasonable effectiveness of transfer learning on natural language processing

David Low (Pand.ai)

Transfer
Learning has been proven to be a tremendous success in the Computer
Vision field as a result of ImageNet competition. In the past months,
the Natural Language Processing field has witnessed several
breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit
and BERT. In this talk, David will be showcasing the use of transfer
learning on NLP application with SOTA accuracy.

The future of machine learning is decentralized

Alex Ingerman (Google)

Federated
Learning is the approach of training ML models across a fleet of
participating devices, without collecting their data in a central
location. Alex Ingerman introduces Federated Learning, compares the
traditional and federated ML workflows, and explores the current and
upcoming use cases for decentralized machine learning, with examples
from Google's deployment of this technology.

Trading strategies using alternative data and machine learning

Arun Verma (Bloomberg)

Arun Verma illustrates the use of AI and ML techniques in quantitative finance that leads to profitable trading strategies. Passive investing (or quantamental investing) is now very popular and many techniques from deep learning and reinforcement learning as well as NLP and sentiment analysis are being used for a broad set of datasets such as news and geolocational data.

Detect the Undetectable at the Breach

Chenta Lee (IBM)

By combining various analytics including DGA, squatting, tunneling, and rebinding detection, we built a DNS analytic playbook to anneal actionable threat intelligence from billions of DNS requests. We will show how DNS volumetric data and analytics complement each other to create an new dimension to look at security postures. Moreover, how to leverage it in security operations?

Game playing using AI on Spark

Shengsheng Huang (Intel)Using AI to play games is often perceived as an early step toward
achieving general machine intelligence, as the ability to reason and
make decisions based on sensed information is an essential part of
general intelligence. Shengsheng Huang takes you through her experiences
from her attempts in using the AI on Spark for playing games.

视频精彩度分析及智能创作

A humane AI solution to improve debt collection

Ying Liu (Abakus 鲸算科技(Wecash闪银）)

AI
debt collection platform of Abakus provides a friendly and humane
product solution which is designed for people who work in the live
agents of the organization in the frontline. The agent training of the
organization could be enhanced more smoothly with an AI friendly
culture. It has been proved in our experiment that the performance of
the collection assistants has been highly improved.

To show case how to build efficient recommender systems for e-commerce industry using deep learning technologies

How AI is revolutionizing the wind power industry

Dongfeng Chen (Clobotics)

In this talk, we will share the successes and failures of creating an entirely autonomous visual recognition-powered drone inspection solution for turbine blades, which increased the efficiency by 10 times.

On-device machine learning gives us the ability to turn this wasted data into actionable information, and will enable a massive number of new applications over the next few years. This talk will cover why embedded machine learning is so important, how it can be implemented on existing chips, and some of the new uses it will unlock.

Keynote with Ion Stoica

Ion Stoica (UC Berkeley)

Keynote with Ion Stoica

－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－－

Bringing research and production together with PyTorch 1.0

Joseph Spisak (Facebook)

Learn
how PyTorch 1.0 enables you to take state-of-the-art research and
deploy it quickly at scale in areas from autonomous vehicles to medical
imaging. We'll deep dive on the latest updates to the PyTorch framework
including TorchScript and the JIT compiler, deployment support, the C++
interface. We will also cover how PyTorch 1.0 is utilized at Facebook to
power AI across a variety of products.

Artificial intelligence meets genomics: accelerating understanding of our genetic make up and use of genome editing to revolutionize medicine

Yue Cathy Chang (TutumGene)

Genome editing has been dubbed as a top technology that could create trillion-dollar markets in the next decade. Recent advancements in the application of AI to genomic editing are accelerating transformation of medicine. We will discuss how AI is applied to genome sequencing, genome editing and their potential to correct mutations, and questions on using genome editing to optimize human health.

Deep prediction: A year in review for deep learning for time series

Aileen Nielsen (Skillman Consulting)

Deep learning for time series analysis has made rapid progress in 2018 and 2019, with advances in the use of both convolutional and recurrent neural network architectures. The state of the art in deep forecasting will be summarized for 2018 and 2019, including use cases in both forecasting and generating time series.

ML ops and Kubeflow pipeline

Kazunori Sato (Google)

Creating
an ML model is just a starting point. To bring the technology into
production service, you need to solve various real-world issues such as:
building a data pipeline for continuous training, automated validation
of the model, version control of the model, scalable serving infra, and
ongoing operation of the ML infra with monitoring and alerting.

AI at ING: the why, how, and what of a data-driven enterprise

Bas Geerdink (ING)

AI
is at the core of ING’s business. We are a data-driven enterprise, with
‘analytics skills’ as a top strategic priority. We are investing in AI,
big data, and analytics to improve business processes such as balance
forecasting, fraud detection and customer relation management. In this
talk, Bas will give an overview of the use cases and technology to
inspire the audience!

Analytics Zoo: Distributed TensorFlow in production on Apache Spark

Yang Wang (Intel)

We
will introduce Analytics Zoo, a unified analytics + AI platform for
distributed TensorFlow, Keras and BigDL on Apache Spark, designed for
production environment. It enables easy deployment, high performance and
efficient model serving for deep learning applications.

AVA: a Cloud-Native deep learning platform at Qiniu

Chaoguang Li (Qiniu)

Bin Fan (Alluxio)

Atlab
Lab at Qiniu Cloud focuses on deep learning for computer vision. Our
team has built a high-performance and cost-effective training platform
based on Cloud for deep learning, called AVA, which deeply integrates
open source software stack including Tensorflow, Caffe, Alluxio and KODO
our own cloud object storage.

Query the planet: Geospatial big data analytics at Uber

Zhenxiao Luo (Uber)

One
of the distinct challenges for Uber is analyzing geospatial big data.
Locations and trips provide insights that can improve business decisions
and better serve users. Geospatial data analysis is particularly
challenging, especially in a big data scenario. For these analytical
requests, we must achieve efficiency, usability, and scalability in
order to meet user needs and business requirements.

Achieving Salesforce-scale machine learning in production

Sarah Aerni (Salesforce Einstein)

At
Salesforce Einstein data science is an agile partner to over 100,000
customers. How do we achieve this scale? We share lessons learned in
business, technology and process along the way. Via use cases,
oft-missed foundational elements for deployment, and the evaluations
that must happen along the way, we will share how to achieve and sustain
models in production, and where to go from there.

Architecting AI applications

Mikio Braun (Zalando SE)

Mikio Braun looks back on the past 20 years of machine learning research to explore aspects of artificial intelligence. He then turns to current examples like autonomous cars and chatbots, putting together a mental model for a reference architecture for artificial intelligence systems.

Best practice of building data science platform in Rakuten

安敖日奇朗 (Rakuten, Inc.)

TzuLin Chin (Rakuten, Inc.)

Data
Science Platform is a suite of tools for exploring data, training
models, and running GPU/CPU compute jobs in an isolated container
environment. It provides one click machine learning environment
creation, powerful job scheduler and flexible "function as a service"
component. It runs on Kubernetes and supports both on-premises and cloud
environment, as well as hybrid mode.

AI pipelines on container platform

WEIQIANG ZHUANG (IBM)

Huaxin Gao (IBM)

AI pipelines simplifies the lifecycle workflow management and enhances the reproducibility and collaboration for machine learning/deep learning. A cloud native platform solution is great at portability and scalability. Combining both strengths, AI pipelines on container platform can help accelerate both AI applications development and deployment.

Using ML for personalizing food recommendations

Maulik Soneji (Go-jek)

Jewel James (Go-jek)

The
story of how we prototyped the search framework that personalizes the
restaurant search results by using ML to learn what constitutes a
relevant restaurant given a user's purchasing history

We
exploit the good representation capability of AAE (Adversarial
AutoEncoder) in our risk factors modeling in fighting a special kind of
financial frauds. It's one step of our long stack of unsupervised tasks,
yet it's proved to be efficient and effective in our practice.

Enlighten the future of games with artificial intelligence

Renjei Li (NetEase Fuxi Lab)

Theoretical AI research isn't a bottleneck in AI, but finding a good application scenario for AI is. Renjei Li examines how gaming is a great scenario for AI, and he walks you through some of the recent research in the future of AI games with reinforcement learning, natural language processing (NLP), computer vision and graphics, and user persona and virtual human.

Low -precision inference on Intel Architecture

Lei Xia (Intel)

Vector
Neural Network Instructions or VNNI is the new Intel instruction set
for low precision AI inference inside next generation Xeon platform.
This lecture is to introduce the features of the VNNI and Intel software
tools to support developers to use this new instruction set to
accelerate inference with INT8.

The TensorFlow library provides for the use of computational graphs,
with automatic parallelization across resources. This architecture is
ideal for implementing neural networks. This training will introduce
TensorFlow's capabilities in Python. It will move from building machine
learning algorithms piece by piece to using the Keras API provided by
TensorFlow with several hands-on applications.

09:00-17:00



多功能厅6A+B (Function Room 6A+B)

Deep Learning with PyTorch

Rich Ott (The Data Incubator)

PyTorch is a machine learning library for Python that allows users to
build deep neural networks with great flexibility. Its easy to use API
and seamless use of GPUs make it a sought after tool for deep learning.
This course will introduce the PyTorch workflow and demonstrate how to
use it. Students will be equipped with the knowledge to build deep
learning models using real-world datasets.

Undesired
bias in machine learning has become a worrying topic due to the
numerous high profile incidents. In this talk we demystify machine
learning bias through a hands-on example. We'll be tasked to automate
the loan approval process for a company, and introduce key tools and
techniques from latest research that allow us to assess and mitigate
undesired bias in our machine learning models.

13:30-17:00



Design thinking for AI

Chris Butler (IPSoft)

Purpose,
a well-defined problem, and trust from people are important factors to
any system, especially those that employ AI. Chris Butler leads you
through exercises that borrow from the principles of design thinking to
help you create more impactful solutions and better team alignment.

In this tutorial, we will show how to build and productionize deep
learning applications for Big Data using "Analytics
Zoo":https://github.com/intel-analytics/analytics-zoo (a unified
analytics + AI platform that seamlessly unites Spark, TensorFlow, Keras
and BigDL programs into an integrated pipeline) using real-world use
cases (such as JD.com, MLSListings, World Bank, Baosight, Midea/KUKA,
etc.)

13:30-17:00



Building reinforcement learning models and AI applications with Ray

Richard Liaw (UC Berkeley RISELab)

Ray
is a general purpose framework for programming your cluster. We will
lead a deep dive into Ray, walking you through its API and system
architecture and sharing application examples, including several
state-of-the-art AI algorithms.

Almost
every business today uses forecasting to make better decisions and
allocate resources more effectively. Deep learning has achieved a lot of
success in computer vision, text and speech processing, but has only
recently been applied to time series forecasting. In this tutorial we
show how and when to apply deep neural networks to time series
forecasting. The tutorial will be in CHN and EN.

Intelligent experiences powered by AI can seem like magic to users.
Developing them, however, is pretty cumbersome involving a series of
sequential and interconnected decisions along the way that are pretty
time consuming. What if there was an automated service that identifies
the best machine learning pipelines for a given problem/data? Automated
machine learning does exactly that!

The unreasonable effectiveness of transfer learning on natural language processing

David Low (Pand.ai)

Transfer
Learning has been proven to be a tremendous success in the Computer
Vision field as a result of ImageNet competition. In the past months,
the Natural Language Processing field has witnessed several
breakthroughs with transfer learning, namely ELMo, Transformer, ULMFit
and BERT. In this talk, David will be showcasing the use of transfer
learning on NLP application with SOTA accuracy.

13:10-13:50



The future of machine learning is decentralized

Alex Ingerman (Google)

Federated
Learning is the approach of training ML models across a fleet of
participating devices, without collecting their data in a central
location. Alex Ingerman introduces Federated Learning, compares the
traditional and federated ML workflows, and explores the current and
upcoming use cases for decentralized machine learning, with examples
from Google's deployment of this technology.

14:00-14:40



Trading strategies using alternative data and machine learning

Arun Verma (Bloomberg)

We illustrate use of AI and ML techniques in Quantitative finance that lead to profitable trading strategies. Passive investing (or Quantamental investing) is now very popular and many techniques from deep learning, reinforcement learning as well as NLP and sentiment analysis are being used for a broad set of data sets such as News and Geolocational data.

14:50-15:30



Detect the Undetectable at the Breach

Chenta Lee (IBM)

By
combining various analytics including DGA, squatting, tunneling, and
rebinding detection, we built a DNS analytic playbook to anneal
actionable threat intelligence from billions of DNS requests. We will
show how DNS volumetric data and analytics complement each other to
create an new dimension to look at security postures. Moreover, how to
leverage it in security operations?

16:20-17:00



Game playing using AI on Spark

Shengsheng Huang (Intel)In this presentation we will share experiences from our attempts in using AI on Spark for game playing.

ONNX:开放和互操作平台让AI无处不在(AI everywhere: Open and interoperable platform for AI with ONNX)

Henry Zeng (Microsoft)，Emma Ning (Microsoft）

An
open and interoperable ecosystem enables you to choose the framework
that's right for you, train at scale, and deploy to cloud and edge. ONNX
provides a common format supported by many popular frameworks and
hardware accelerators. This session provides an introduction to ONNX and
its core concepts. The session will be delivered in English and Chinese
jointly.

14:00-14:40



Exciting new features in TensorFlow 2.0

Tiezhen Wang (Google)

TensorFlow
2.0 is a major milestone with a focus on ease of use. This talk will
give a in depth introduction to the new exciting features and best
practices. Topics such as distributed strategies and edge deployment
(TensorFlow Lite and TensorFlow.js) will also be covered.

AI
debt collection platform of Abakus provides a friendly and humane
product solution which is designed for people who work in the live
agents of the organization in the frontline. The agent training of the
organization could be enhanced more smoothly with an AI friendly
culture. It has been proved in our experiment that the performance of
the collection assistants has been highly improved.

To show case how to build efficient recommender systems for e-commerce industry using deep learning technologies

16:20-17:00



How AI is revolutionizing the wind power industry

Dongfeng Chen (Clobotics)

In
this talk, we will share the successes and failures of creating an
entirely autonomous visual recognition-powered drone inspection solution
for turbine blades, which increased the efficiency by 10 times.

13:10-17:00



紫金大厅B（Grand Hall B)

11:15-11:55



Forecasting customer activities with dilated convolution neural networks: Use case and best practices

Tao Lu (Microsoft)，Chenhui Hu (Microsoft)

Forecasting
customer activities is one of the most important and common business
problems. In Microsoft Azure Identity team, we forecast customer
behavior based on billions of user activities. We will share how we
improve 25% of forecasting accuracy with dilated convolutional neural
networks and reduce 80% of the time in development with the best
practices of time series forecasting.

13:10-13:50



Efficient deep learning for the edge

Bichen Wu (UC Berkeley)

The
success of deep neural networks is attributed to three factors:
stronger computing capacity, more complex neural networks, and more
data. These factors, however, are usually not available with the edge
applications as autonomous driving, AR/VR, IoT, and so on. In this talk
we discuss how we apply AutoML, SW/HW codesign, domain adaptation to
solve these problems.

14:00-14:40



TensorFlow lite for microcontrollers

Pete Warden (Google)

Pete
Warden explores how you can use Google's open source framework to run
machine learning models on embedded processors like microcontrollers and
DSPs. Discover what you need to get started using the code itself,
including hardware, coding tools, and getting the library built.

14:50-15:30



Using deep learning and time-series forecasting to reduce transit delays

Toronto is unique among North American cities for having a legacy
streetcar network as an integral part of its transit system. This means
streetcar delays are a major contributor to gridlock in the city. Using
deep learning and time-series forecasting, we'll show how streetcar
delays can be predicted... and prevented.

Theoretical
AI research isn't a bottleneck in AI, but finding a good application
scenario for AI is. Renjei Li examines how gaming is a great scenario
for AI, and he walks you through some of the recent research in the
future of AI games with reinforcement learning, natural language
processing (NLP), computer vision and graphics, and user persona and
virtual human.

Vector Neural Network Instructions or VNNI is the new Intel instruction
set for low precision AI inference inside next generation Xeon platform.
This lecture is to introduce the features of the VNNI and Intel
software tools to support developers to use this new instruction set to
accelerate inference with INT8.

We exploit the good representation capability of AAE (Adversarial
AutoEncoder) in our risk factors modeling in fighting a special kind of
financial frauds. It's one step of our long stack of unsupervised tasks,
yet it's proved to be efficient and effective in our practice.

At Salesforce Einstein data science is an agile partner to over 100,000
customers. How do we achieve this scale? We share lessons learned in
business, technology and process along the way. Via use cases,
oft-missed foundational elements for deployment, and the evaluations
that must happen along the way, we will share how to achieve and sustain
models in production, and where to go from there.

13:10-13:50



Architecting AI applications

Mikio Braun (Zalando SE)

Mikio
Braun takes you back through the past 20 years of machine learning
research to explore aspects of artificial intelligence, then to current
examples like autonomous cars and chatbots. Together you'll put together
a mental model for a reference architecture for artificial intelligence
systems.

14:00-14:40



Best practice of building data science platform in Rakuten

安敖日奇朗 (Rakuten, Inc.), TzuLin Chin (Rakuten, Inc.)

Data Science Platform is a suite of tools for exploring data, training
models, and running GPU/CPU compute jobs in an isolated container
environment. It provides one click machine learning environment
creation, powerful job scheduler and flexible "function as a service"
component.
It runs on Kubernetes and supports both on-premises and cloud
environment, as well as hybrid mode.

14:50-15:30



AI Pipelines on container platform

WEIQIANG ZHUANG (IBM)，Huaxin Gao (IBM)

AI
pipelines simplifies the lifecycle workflow management and enhances the
reproducibility and collaboration for machine learning/deep learning. A
cloud native platform solution is great at portability and scalability.
Combining both strengths, AI pipelines on container platform can help
accelerate both AI applications development and deployment.

16:20-17:00



Using ML for personalizing food recommendations

Maulik Soneji (Go-jek), Jewel James (Go-jek)

The story of how we prototyped the search framework that personalizes
the restaurant search results by using ML to learn what constitutes a
relevant restaurant given a user's purchasing history

11:15-17:00



报告厅（Auditorium)

11:15-11:55



Analytics Zoo: Distributed TensorFlow in production on Apache Spark

Yang Wang (Intel)

We will introduce Analytics Zoo, a unified analytics + AI platform for
distributed TensorFlow, Keras and BigDL on Apache Spark, designed for
production environment. It enables easy deployment, high performance and
efficient model serving for deep learning applications.

Atlab Lab at Qiniu Cloud focuses on deep learning for computer vision.
Our team has built a high-performance and cost-effective training
platform based on Cloud for deep learning, called AVA, which deeply
integrates open source software stack including Tensorflow, Caffe,
Alluxio and KODO our own cloud object storage.

14:50-15:30



Query the planet: Geospatial big data analytics at Uber

Zhenxiao Luo (Uber)

One of the distinct challenges for Uber is analyzing geospatial big
data. Locations and trips provide insights that can improve business
decisions and better serve users. Geospatial data analysis is
particularly challenging, especially in a big data scenario. For these
analytical requests, we must achieve efficiency, usability, and
scalability in order to meet user needs and business requirements.

Learn how PyTorch 1.0 enables you to take state-of-the-art research and
deploy it quickly at scale in areas from autonomous vehicles to medical
imaging. We'll deep dive on the latest updates to the PyTorch framework
including TorchScript and the JIT compiler, deployment support, the C++
interface. We will also cover how PyTorch 1.0 is utilized at Facebook to
power AI across a variety of products.

13:10-13:50



Artificial intelligence meets genomics: accelerating understanding of our genetic make up and use of genome editing to revolutionize medicine

Yue Cathy Chang (TutumGene)

Genome editing has been dubbed as a top technology that could create trillion-dollar markets in the next decade. Recent advancements in the application of AI to genomic editing are accelerating transformation of medicine. We will discuss how AI is applied to genome sequencing, genome editing and their potential to correct mutations, and questions on using genome editing to optimize human health.

14:00-14:40



Deep prediction: A year in review for deep learning for time series

Aileen Nielsen (Skillman Consulting)

Deep
learning for time series analysis has made rapid progress in 2018 and
2019, with advances in the use of both convolutional and recurrent
neural network architectures. The state of the art in deep forecasting
will be summarized for 2018 and 2019, including use cases in both
forecasting and generating time series.

14:50-15:30



ML ops and Kubeflow pipeline

Kazunori Sato (Google)

Creating an ML model is just a starting point. To bring the technology
into production service, you need to solve various real-world issues
such as: building a data pipeline for continuous training, automated
validation of the model, version control of the model, scalable serving
infra, and ongoing operation of the ML infra with monitoring and
alerting.

16:20-17:00



AI at ING: The why, how, and what of a data-driven enterprise

Bas Geerdink (ING)

AI is at the core of ING’s business. We are a data-driven enterprise,
with ‘analytics skills’ as a top strategic priority. We are investing in
AI, big data, and analytics to improve business processes such as
balance forecasting, fraud detection and customer relation management.
In this talk, Bas will give an overview of the use cases and technology
to inspire the audience!